Data Stories is our blog series highlighting the cool and unusual ways people use data. I was intrigued by a presentation that Eric Colson gave to Strata about Stitch Fix, a personal shopping site, that relied heavily on data along with its personal shoppers. This was a fun interview for us because several of my female colleagues order Stitch Fix boxes filled with items Stitch Fix thinks they might like. It’s amazing to see how data impacts even fashion. As a side note, this is Gnip’s 25th Data Story, so be on the watch for a compilation of all of our amazing stories.
1. Most people think of Stitch Fix as personal shopping service, powered by professional stylists. But, behind the scenes you are also using data and algorithms. Can you explain how this all works?
We use both machine processing and expert-human judgment in our styling algorithm. Each resource plays a vital role. Our inventory is both diverse and vast. This is necessary to ensure we have relevant merchandise for each customer’s specific preferences.
2. What do you think would need to change if you ever began offering a similar service for men?
We would likely need entirely new algorithms and different sets of data. Men are less self-aware of how things should fit on them or what styles would look good on them (at least, I am!). Men also shop less frequently, but typically indulge in bigger hauls when they do. Also, the styles are less fluid for men and we tend to be more loyal to what is tried & true. In fact, a feature to ”send me the same stuff I got last time” might do really well with men. In contrast, our female customers would be sorely disappointed if we ever sent them the same thing twice!
So, while the major technology pieces of our platform are general enough to scale into different categories, we’d still want to collect new data and development different algorithms and features to accommodate Men.
So, in some ways Stitch Fix already had edge over Netflix with respect to data. That said, the Netflix ethos for democratizing innovation has permeated into the Stitch Fix culture. Like Netflix, we try not to let our biases and opinions blind us as we try new ideas. Instead, we take our beliefs for how to improve the customer experience and reformulate them as hypotheses. We then run an AB test and let the data speak for itself. We either reject or accept the hypothesis based on the observed outcome. The process takes emotion and ego away and allows us to make better decisions.
Also, like Netflix, we invest heavily in our data and algorithms. Both companies recognize the differentiating value in finding relevant things for their customers. In fact, given our business model, algorithms are even more crucial to Stitch Fix than they are to Netflix. Yet, it was Netflix which pioneered the framework for establishing the capability as strategic differentiator.
Given our unique data, we are able to pioneer new techniques for most business processes. For example, take the process of sourcing and procuring our inventory. Since we have the capability of getting the right merchandise in front of the right customer, we can do more targeted purchasing. We don’t need to make sweeping generalization about our customer base. Instead, we can allow each customer to be unique. This allows us to buy more diverse inventory in smaller lots since we know we will be able to send it only to the customers for which it is relevant.
We also have the inherent ability to improve over time. With each shipment, we get valuable feedback. Our customers tell us what they liked and didn’t like. They give us feedback on the overall experience and on every item they receive. This allows us to better personalize to them for the next shipment and even allows us to apply the learnings to other customers.
5. Your stylists will sometimes override machine-generated recommendations based on other information they have access to. For example, customers can put together a Pinterest board so that they can show the stylist things they like. Do you think machines will ever process this data?
No time soon! Processing unstructured data such as images and raw text are squarely in the purview of humans. Machines are notoriously challenged when it comes extracting the meaning that is conveyed in this type of information. For example, when a customer pins a picture to a Pinterest board, often they are expressing their fondness for a general concept, or even an aspiration, as opposed to the desire for a specific item. While machine learning has made great strides in processing unstructured data, there is still a long ways to go before they can be reliable.
Thanks to Eric for the interview! If you have suggestions for other Data Stories, please leave a comment!